Applied Researcher I

Capital One Financial

Confirmed live yesterday Trusted

Quick summary

Work type
On-site
Location
San Jose, CASan Francisco, CAMcLean, VACambridge, MANew York, NY
Salary
$218,700–$249,600 / yr
Posted
52 days ago
Freshness
Confirmed live yesterday
Nearby
99+ roles within 25 mi

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $216k
This role $234k
$153k most similar roles pay here $284k

This role pays more than 60% of similar roles. Most pay $177,250–$254,750 — the shaded band above. At the midpoint, this role pays about $234k versus about $216k for comparable roles.

Based on 240 similar postings.

Employer

About Capital One Financial

Capital One Financial is a bank holding company specializing in credit cards, auto loans, banking, and savings products, known for its data-driven approach to consumer and commercial finance. Industry: Financial Services & Banking

Capital One Financial currently has 998 open roles on FindRole.

Listed pay typically runs $197,300–$225,100 across 992 roles with salary data.

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View all roles at Capital One Financial

At a glance

TL;DR · Applied Researcher I

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The Applied Researcher I joins the AI Foundations team to develop advanced machine learning solutions that transform how customers interact with their money. Working within a cross-functional team of data scientists, software engineers, and product managers, you will build AI foundation models through every stage of development, including design, training, evaluation, validation, and implementation. You will leverage a technical stack featuring PyTorch, AWS Ultraclusters, Huggingface, Lightning, and VectorDBs to extract insights from massive volumes of numeric and textual data. The role focuses on high-impact applied research to integrate state-of-the-art AI developments into production systems. Key responsibilities include managing the research lifecycle, addressing complex technical problems, and translating sophisticated machine learning concepts into tangible business goals while ensuring robust performance across large-scale training data and inference volumes for banking applications.

Skills

Pytorch AWS Huggingface Lightning VectorDBs LLM NLP Graph Neural Networks RLHF Quantization Model Sparsification Training Parallelism Supervised Finetuning Instruction-Tuning pytorch-geometric DGL

What you'll do

  • Build AI foundation models through all phases of development including design, training, evaluation, and validation.
  • Conduct high-impact applied research to integrate state-of-the-art AI developments into customer experiences.
  • Use a broad technology stack including PyTorch, Hugging Face, and VectorDBs to analyze large volumes of data.
  • Develop scalable machine learning models for production use across various data types like text, images, and graphs.
  • Translate complex technical research into tangible business goals for stakeholders and product managers.
  • Identify and solve large, undefined problems by researching and evaluating emerging technologies.
  • Own and pursue a research agenda by selecting impactful problems and executing long-running projects autonomously.

What we're looking for

  • Must have a PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or a related field.
  • Alternatively, must have an M.S. in a relevant field plus 2 years of experience in Applied Research.
  • Must have hands-on experience developing AI foundation models and solutions using open-source tools and cloud computing platforms.
  • Must possess a deep understanding of the foundations of AI methodologies.
  • Must have experience building large deep learning models for language, images, events, or graphs.
  • Must demonstrate an engineering mindset with a track record of delivering models at scale for training and inference.
  • Must have a track record of high-quality machine learning ideas, such as first author publications or significant projects.
  • Must be able to independently own and pursue a research agenda including selecting problems and managing long-running projects.

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